Later, our relaxed spatio-temporal aggregation method (we will explain the details of the method in Section 4) takes these deep features as input. Since the real-fake expression dataset has a limited number of samples, we select dense and short-time temporal intervals from videos to augment data (i.e. the length of each interval is set to 150 ms) in order to learn micro-emotional representations. After learning optimum parameter sets from the training data, spatio-temporal representations are computed from dense and overlapping intervals, and they are aggregated with [1]} to obtain a single video-level representation.
